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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Machine Learning in Predicting Drug-Drug Interactions: Enhancing Patient Safety |
| Authors Name: | Aadarsh joshi , Anamika pant |
| Unique Id: | IJSDR2409029 |
| Published In: | Volume 9 Issue 9, September-2024 |
| Abstract: | In clinical exercise and drug improvement, the prediction of drug-drug interactions (DDIs) is a crucial component of patient protection. The human curation of statistics and clinical observations utilized in conventional methods of figuring out DDIs may be exhausting and prone to blunders. The method of forecasting DDIs has been substantially advanced with the creation of system getting to know (ML), imparting extra precise, powerful, and scalable answers. using device getting to know algorithms to predict DDIs is tested in this paintings, with a specific emphasis on deep gaining knowledge of, ensemble techniques, and natural language processing (NLP). ML models can as it should be stumble on feasible DDIs by way of utilizing a plethora of biomedical records, which includes remedy traits, molecular interactions, and patient information. The prediction strength of these fashions is further greater by the incorporation of real records from pharma covigilance databases and electronic fitness information (EHRs). by lowering the opportunity of adverse drug reactions, using system mastering (ML) to anticipate drug-drug interactions (DDIs) improves patient protection and facilitates optimize drug remedy regimens, which in turn cause extra individualized and green healthcare. The problems with ML-based totally DDI prediction are also included on this observe, alongside feasible answers to those troubles. Those problems encompass data satisfactory, model interpretability, and the requirement for reliable validation techniques. The outcomes highlight how gadget getting to know may be modern in defensive affected person safety and enhancing pharmaceutical studies. |
| Keywords: | Key Words: Drug-Drug Interactions (DDIs), Machine Learning (ML), Patient Safety, Adverse Drug Reactions, Deep Learning, Ensemble Methods, Natural Language Processing (NLP), Biomedical Data, Electronic Health Records (EHRs), Pharmacovigilance, Personalized Medicine, Predictive Modeling, Healthcare Optimization. |
| Cite Article: | "Machine Learning in Predicting Drug-Drug Interactions: Enhancing Patient Safety", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.9, Issue 9, page no.272 - 275, September-2024, Available :http://www.ijsdr.org/papers/IJSDR2409029.pdf |
| Downloads: | 00025 |
| Publication Details: | Published Paper ID: IJSDR2409029 Registration ID:212464 Published In: Volume 9 Issue 9, September-2024 DOI (Digital Object Identifier): Page No: 272 - 275 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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